Format of the dataset

In order to properly feed the dataset to IGNNITION, the dataset must be in json format. For this, the user must generate json files –potentially many– with the help of the well-known library Networkx, each of which can optionally be compressed into a .tar.gz file. Furthermore, IGNNITION requires that the user manually separetes the training from the evalutation set into two different directories, the paths of which must be specified in the training_options.yml file in their corresponding filds (check train and evaluate).

We would like to highlight that the dataset can contain potentially many json files as well as many tar.gz files, each of which compressing one single json file. The only restriction in this regard is that the json files are valid and follow the scheme that we present below.

What should the dataset include?

In order to generate the dataset to be fed to IGNNITION, the user must generate the corresponding graphs with the help of the well-known library networkx. Moreover, this library allows that, after each of the corresponding graphs is created, it can easily be serialized as a json file. When designing the different graphs, however, one must remember that most of the fields require references of values, which enables our model description to be totaly agnostic to the actual dataset. The reason is that in execution time, IGNNITION will gather the corresponding values that each of this references point to. Ultimately, this design principle, thus, requires users to make only minor changes in the model to adapt it to a completely different dataset.

This principle, however, imposes an important constraint that all the references used in the model descprition file match the ones used in the dataset. Below we provide a brief description of how a user can ensure that this constraint is satisfied. Nevertheless, IGNNITION incorporates an error-checking system (further explained in debugging assistant]), which assists users in the debugging of such aspects.

How to generate a sample?

We now review how we can generate a general sample which should give the user a good intuition to potentially build more complex examples.

Create the graph

First of all, the user must create a general grap using the calls from below.

    import networkx as nx
G = nx.DiGraph()


Notice, however, that the call presented below creates a directed graph, but this needs not to be the case. Alternatively, the user may define an undirected graph as follows:

    import networkx as nx
G = nx.Graph()


Create the nodes

Now we must populate this graph with the corresponding nodes. For this it is important to remember that we are considering a general case in which we can have nodes of different types, and which thus, must be treated differently. Consequently, each of the nodes must include a field called entity which value is the name of the entity of the node. For simplicity, let us consider a simple case with two entity types entity1 and entity2.

Apart from this field entity, the user must also include for each of the nodes as many fields as features where defined in the model description file. For instance, let us consider a case in which we define in the model description file a single feature f1 for nodes of entity1 and a feature named f2 for nodes of entity2. Below we show how such nodes could be created.

     G.add_node('node1',
entity='entity1',
f1=v_1)

entity='entity2',
f2=v_2)


Notice that the value assigned to each of the features might not be a single integer, as it could be an array of values which IGNNITION will automatically identify and treat appropriately.

Create edges

Now that all the nodes are created, we just need to create the edges between this nodes. For simplicity, let us suppose we want to add an edge between the two previous nodes node1 and node2. In some cases, moreover, we might want to include information regarding this edge which can be later referenced in the model description file. E.g., in examples of the field of chemistry, we might want to include a feature indicating the type of bond that this edge represents. To do so, we follow exactly the same idea as before, and we also include in the definition of the edge the name of the parameter and its value.

    G.add_edge('node1', 'node2', edge_param1= v_3)


Defining the label

Finally, we just need to include the information of the label. In this case, it is worth remembering that GNNs can work either in node label or in graph label. The first will hence aim to make single predictions over potentially every node of the graph, and the second over the whole graph.

Node level

In this type of problems, we must define a label for each of the nodes, or at least for each of the nodes belonging to a certain entity type. To do so, we just need to add a new parameter to each of the nodes that we created before. To do so, we can simply add this parameter when first created the node. Otherwise, we can do it as follows:

    G.nodes['node1'][my_label_name] = l


Again, l may or may not be a single integer. Moreover, note that my_label_name must match with the name of output_label used in the model_description file.

Graph level

The second option is that we aim to make predicitons over the whole graph. In this case we need to add this information, not for each of the nodes but to the entire graph. To do so, again using the name used in the model_description file, we proceed as follows:

    G.graph[my_label_name] = l


Serializing the graph

Now that we have created a sample, we just need to serialize it to be able to save it as a json file. For this, use the code from below:

    from networkx.readwrite import json_graph
training_data = []
training_data.append(parsed_graph)


At this point we might want to accumulate many of them before writting the file using the traning_data array. In any case, once we want to write this information as a file, use the code from below:

    import json
with open('data.json', 'w') as json_file:
json.dump(training_data, json_file)


Compress the file

This is an optional step, but which we recommend since it can help to considerably reduce the memory size of the dataset. This step consists on compressing the file we just created so as to mantain a dataset of compressed files. For this, use the following python instructions:

    import tarfile
tar = tarfile.open(path + "/sample_" + str(file_ctr) + ".tar.gz", "w:gz")